Who starts good ends even better? I am pretty sure that on your data journey you came across some courses, videos, articles, maybe use cases where someone takes some data.
I am pretty sure that on your data journey you came across some courses, videos, articles, maybe use cases where someone takes some data, builds a classification/regression model, shows you great results, you learn how that model works and why it works that way and not another and everything seems to be fine. You think you just learned a new thing (and you did), you are happy about that (yes, you are ! I am not kidding around here, you’re doing great!) and you continue to the next piece of content.
But later on you start to ask additional questions (everyone has different length of that “later on”), like: where did that data come from? and if I have more data, will that model run so smoothly as it did during the demonstration? does the data in real world exist in such format? can I get similar data and if I can will it be so easy to process? what did the results of that model mean? can I present that data in prettier way? and so on and so on and so on.
When I started to learn about data analytics, data science, world of data in general I was always amused by the results people will get after processing some piece of data, or after running a machine learning model or after getting keys from word buckets etc. But every time I would try to do something on my own it will always appear a new obstacle: the data I would like to analyze is too much or not enough, one model will run with one piece of data, but it won’t with another etc etc.
After having all these difficulties and learning to deal with them the hard way I would like to share with the essential 5 Vs of data that you have to have taken care of before you start your data project/solution.
When we talk “volume” in regards of data we have to be aware of amount of data that has to be handled in the project — should we use several servers to handle that volume and distribute the load between them? or maybe our computer with our own hard disk is quite enough to solve the problem?
Velocity is the speed with which data travels through our model/project/solution. The speed with which it is ingested, processed and delivered to the end client. We have to be aware if this is real-time data, near real-time or maybe this is just historic data which is not going anywhere soon and we can talk her out slowly and efficiently 😉
Data science is omnipresent to advanced statistical and machine learning methods. For whatever length of time that there is data to analyse, the need to investigate is obvious.
A data scientist/analyst in the making needs to format and clean data before being able to perform any kind of exploratory data analysis.
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